Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction

A UV-Vis spectroscopic detection method for cobalt ions in zinc sulfate solution based on discrete wavelet transform and extreme gradient boosting

Zinc is a crucial strategic metal resource. The concentration of cobalt ions in zinc refining solution significantly impacts the efficiency of zinc electrolysis production. The traditional method of detecting cobalt ions in zinc solution is time-consuming, labor-intensive and ineffective. However, optical detection offers the advantage of high efficiency and low cost, making it a potential replacement for the traditional method. In this study, the spectral curve of cobalt ions in zinc solution is detected by ultraviolet-visible (UV-Vis) spectrophotometry. Additionally, we propose a model for the concentration-absorbance relationship of cobalt ions in zinc solution based on discrete wavelet transform and extreme gradient boosting (DWT-XGBoost) algorithms. First, the spectral curve's information region is denoised by using Savitzky-Golay (S-G) smoothing. Then, the denoised spectra is utilized to extract features through discrete wavelet transform and principal component analysis. These features are used as inputs to the XGBoost model to establish prediction models for low and high cobalt ions in zinc solution. Bayesian optimization is implemented to adjust the model's hyperparameters, including learning rate, feature sampling ratio, to enhance the prediction performance. Finally, applying the model to zinc solution samples from a zinc smelter and compared with other state-of-the-art algorithms, the DWT-XGBoost algorithm exhibits the lowest RMSE, MAE and MAPE, with values of 0.034 mg/L, 0.025 mg/L, 6.983 % for low cobalt and with values of 0.231 mg/L, 0.067 mg/L and 0.472 % for high cobalt. The experimental results demonstrate that the DWT-XGBoost model exhibits significantly superior prediction performance.

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Multi-Task Time Series Forecasting Based on Graph Neural Networks

Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.

Location estimation based on feature mode matching with deep network models

Introduction

Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements.

Methods

A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University.

Results and discussion

The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38-4.15% and 18.42-5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.

A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation

The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species' continuous and rapid decline, information and intelligent technology have provided innovative knowledge about how functional biological diversity interacts with environmental changes. Especially in complex natural scenes, identifying bird species with a real-time and accurate pattern is vital to protect the ecological environment and maintain biodiversity changes. Aiming at the fine-grained problem in bird image recognition, this paper proposes a fine-grained detection neural network based on optimizing the YOLOV5 structure via a graph pyramid attention convolution operation. Firstly, the Cross Stage Partial (CSP) structure is introduced to a brand-new backbone classification network (GPA-Net) for significantly reducing the whole model's parameters. Then, the graph pyramid structure is applied to learn the bird image features of different scales, which enhances the fine-grained learning ability and embeds high-order features to reduce parameters. Thirdly, YOLOV5 with the soft non-maximum suppression (NMS) strategy is adopted to design the detector composition, improving the detection capability for small targets. Detailed experiments demonstrated that the proposed model achieves better or equivalent accuracy results, over-performing current advanced models in bird species identification, and is more stable and suitable for practical applications in biodiversity conservation.

Optimization Algorithm of Moving Object Detection Using Multiscale Pyramid Convolutional Neural Networks

Object detection and recognition is a very important topic with significant research value. This research develops an optimised model of moving target identification based on CNN to address the issues of insufficient positioning information and low target detection accuracy (convolutional neural network). In this article, the target classification information and semantic location information are obtained through the fusion of the target detection model and the depth semantic segmentation model. The classification and position portion of the target detection model is provided by the simultaneous fusion of the image features carrying various information and a pyramid structure of multiscale image features so that the matched image fusion characteristics can be used by the target detection model to detect targets of various sizes and shapes. According to experimental findings, this method's accuracy rate is 0.941, which is 0.189 higher than that of the LSTM-NMS algorithm. Through the migration of CNN and the learning of context information, this technique has great robustness and enhances the scene adaptability of feature extraction as well as the accuracy of moving target position detection.